{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FormatStrFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = [1, 11,21,31,41,51,61,71,81,91]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yahoo \n",
    "y_1 = np.array([73.1, 74.225, 74.405, 74.485, 74.633, 74.65, 74.7183, 74.705, 74.6683, 74.6]) + 2.6\n",
    "# dbpedia\n",
    "y_2 = np.asarray([98.5071, 98.8943, 98.9343, 98.9671, 98.97, 98.9257, 98.94, 98.92, 98.94, 98.91])+0.05\n",
    "# yelp\n",
    "y_3 = [93.7684, 94.66, 95.1947, 95.2854, 95.2316, 95.1474, 95.1759, 95.1868, 95.3105, 95.2316]\n",
    "\n",
    "# yelp full\n",
    "y_4 = [61.09,63.98,64,63.9,64.01,63.926,63.91,64.088,63.772,64.09]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iCTc8zXmuEJFuGP/O0cDjQKaI/I1poKcBtYDywFqMz+d/SsuQHXIbT9vzDEU2\nbtwI8D2AqqrjNjcBqCEiERiXt2N5qvXHeHBMw/gUP1Bc+dbHM7zxGM/TMYSfAp+KSFugNyenHt4K\n/JQ7RF1pYsWKZZQvX5527doHWxWLH2jZshXAABGZDnQD6gMpwNsYz42DnLqDrRbQGBgANAVmiEhr\nVXUVJKegTRjr1ydSs2ZN2rVrHpQFo2D7uIa7fK+DIavqWkwPs0yQmZnJmjV/0qZNOypWrBhsdSx+\n4NJLBzJu3POHMCEREzDxE14HeqnqGhEZhYmNMCpXtb1AohP4RkXkKBCHyYqQL/ltwtiyZRcpKSnE\nx/dgz55Unz6XN4TTRohgyvdkoEM2h1Fi4loyMjLsfGcI4yzY/KSqPYEvMP7E+zCLRwA7MNG7cjMf\nuEhEIkSkHmaBaW9RZa9fr7hcLrtYFMYUNQ1HmcHuLAp9GjRoBHCviDwKHABuwqy8/09EsoBM4GY4\nsQlDVb8RkXOB3zGdh1HFmac/kafdGs9wJYSNp11pD3WqVauGqvbJc3oHZiX9JHJvwlDVB0sqWzUR\nsHnaw5mQM561a1c96fiCC078H+3adShvcYulSORtX5dddvHxz7Z9hRdezXmKyDgROcPfylgsFktZ\nwdsFo4GYHUcrRGSMiNT1p1IWi8VS2vHKeKqqAPEYn7kHgC0iMltErheRGD/qZ7FYLKUSr12VVPUP\nVb0X44g8ANiECVu3U0Q+EZHz/aOixWKxlD6K7OfpBFk4ABwGjgKVgPbAHGdYb7fzWCyWkMfr1XYR\naYcJXHwtZovmWkwK4f+o6g5nHvRb4H+UkqhKFovF4i8iXK4Ct/QeR0RWA20xOzH+C3yiqsvyKfck\ncI+q1vC1ohaLxVKa8LbnqcCjwHeFJGObDPynxFpZLBZLKcernieAiDQDzlHVyc6xADcAH6jqVv+p\naLFYLKUPb53ke2Lyt+eOfVgdE/h4uYjYBEEWiyWs8Ha1/SXgB+B4CkpVXQS0AH7BhAGzWCyWsMFb\n43km8I6qnhSV24mJOB6TisNisVjCBm+N5wGgoNhbLTC5iywWiyVs8Ha1/TPgWRHZD3yjqodEpApm\np9FzmFQdYYeIvAN0wSykZTvnymEim89V1cc81H0WqOzs2vJW3nzMDq+DgAuoCCwGblfVox7qjQQG\nqOrlRZDVEJiqqr1EpDnwgqpe7W19S+AIQjv8D3A+sDvPpX6qWmBEfhHZhrEZtYBXVLWjtzJLI94a\nz8eAVhiV+0wRAAAgAElEQVQ3JJeIHMMkfosAvgEe9o96pZ77gSWY53/WOfcwJu3tk36SOdqdDlpE\nIoGvMMn3/uVLIY4HRS/nsCnm+7eUToLRDsep6ht+uneZwCvj6fRqBopIB0za1uqY3s8CVV3uR/1K\nNap6VET+CSSIyDeYl8kdQNdcPYDLgUcwL5s04D5VXZz7Ps4b+XPgXExyvXGq+m8v5OeIyDzgAuc+\n5wEvA9GYKOqPquqcPLLOwcQkiAbqAt+r6i0i0gL4EUgGGmLc0OYAtTF50OuLyHeYnm5zd3BhR+Yr\nqmrnvYNEsNthnnv8B1jiNqx5j0OJIu1tV9WVqjpeVZ9X1XfdhtPJ3x6WqOpqzAaCCcDHwE2quh1A\nRFoDY4H+qtoJ06CniUh0PreKVtUuwIXA8062Uo+ISA3gamCeiMRhGv4oVe0AjACmikijPNXuAR5R\n1bMxu8YGOy9FMFklH3eiaO1zni8TuM181EuAfwOXiUg1p86tGONqCSJBaIcPOLEs3D/Dff1MpR2v\nep6OcbwFOA8zz+bOsxoBxACdgLDdkqmqb4vIYGCtqn6f61I/zBzlPLOnADBzlc3zuc07zr22iMgP\nQF/yz1b6uog8xYnvYIZT91JMVsglzn1Wi8hizHeWmxuAS5y8P60xPdDKmN5IJqZn6elZ/xKRWcB1\nIvIZptc70lMdS2AIcDu0w3Yvy72M6bGsAuoARzCTxe2BCsDTftGubLERkzM8N+WA2ap6nfuEsxCz\nPZ/6ube9RgIFJSU7PueZG2f+My+RmGGau0wEJkXvEmA2JohLD04Y4iNO1KzCeBfj2xsFfK6q6YWU\ntwSOQLXDgnBxoj2BsQ8hibfD9quBl5zVsbeA5araDeOmlEyuf1DLSfwEXCwirQBEZCCwAtPby4t7\nDrEJZsg0q4iyFgLtRKSLc5/2mERoP+cqUxPoCDykqtMww/SmmH8uT2SR6ztW1V8x/xT3YofsZYFA\ntsPdmJV/RKQ2+STjCxW8NZ5xmJ4KmG2a3QBUdQfwPMa4WvKgqquA24HPRWQl8AQwsICeWgsRWYYJ\n6zdKVZOLKGsncA0w3omCNRm4QVU35CqzBxgHrBCRpZhV2gWYl6AnVgPlRGRhrnMTgS2qmt+QzlKK\nCGQ7BN4EGotIIvAJJ7+8QwpvQ9Jtx/gSznBWZRWorap7ndXW71Q11s+6hixu/zdVXRFsXbzBmQOf\nAUxQ1a+CrY/FN5S1dhhsvO15zgaecgIipwC7gFGOI+5VwE4/6WcpZThBYHZhhmfTgqyOxRI0vO15\nno4xoHtV9QIRuQnjspKDMcBjVPVNv2pqsVgspYiixPOMAOqr6jbn+DzM3OcfqjrPfypaLBZL6cPb\nnudS4LE8vmMWi8UStnjr59kCkymzVLJ79+GT3gDVq8ewf3/wXA+t/MDJj4urElF4qZJR2tpXadAh\nXOR7al/eLhh9BDwqImeKSCXfqOU/oqIKc1u08kNZvr8pDc8XbB3CXT543/PsBXQA3HvZ0/Jcd6nq\nab5UzGKxWPxJ7dpVC7y2a9ehQut7azy/cX4sxaSkX1SocPDgAdLS0qhXr36wVbGUcnJycti582/q\n1q0XbFXyxduQdGP9rYgltDly5Ajvv/8Ob731OmlpqXTu3JXBg6/hssuuoFatWn6TKyIPAwMx20nf\nU9WPnPNDgLtUtbvfhFuKxYED+/nss6lMnDiBDRtSuO66oTz33MvExMQEW7WT8Daq0tDCyqhqWEaT\nt3gmJyeHr7/+gueeG8v27duoVasWZ53VmYSE31i69A8ef/xfXHBBHwYPvoZ+/S726T+IiPTGBD45\nBxP9637nfCfgJk4OYGEJMqtXr2TixAl89dXnHDlyhIoVK9K4cROmTPmUJUt+58MPP6F164KyARWN\nY8eOFV6oELwdtk8q4LwLyMDkMLLGMx/S0tKYNu1Lj2Wuu+4q4uPPoUePczjzzI6ULx8acVYWL17E\nk08+zLJlS6lQoQJ33TWae+4ZQ9Wqp7Fz599Mm/YlX375OXPmzGLOnFnExlZmwICBDB58DT17nkvd\nutULvLeXUx39MfvypwFVMTEoa2LiMdwLfFjypywaLpeLjIwM0tLSSEtLzfM7jfT0tOOf09JSycnJ\noUOHTnTt2o2aNWv6TI+srCxWrFjG/Pm/Mn/+b2RmZtC+/ZmceWZHzjyzIy1btiIqylvzUHwyMjKY\nMWMaEydOYMmS3wFo1KgJw4bdxJAh1xMbW5mxYx9jwoQP6N+/N88++xLXX39jiWRu2bKZW28tefhR\nb/0881sMqoyJOP08cL2qJpRYm2KS15UkLq4Ku3cfDpY6xMVVYcGCpUyaNIHPPvsvhw4d9LpuTEws\nXbueTY8ePenevSedOp1FxYoViyw/mM9/+PBuRo++nxkzzO7Nyy+/gkcffYrGjZvkW141ka+++pyv\nvvqcrVu3AFCnzuns3Pl3gTLcxtOTK4mIfIiJHDUAEz3qG0xsyn9hwir+T1XjC3uerKxsV0lXdzdu\n3Mh5553Hjh07yM4uapQ3Q+vWrenZs+fxn2bNmhERYR7f/Ts/XC4XOTk5rFy5krlz5zJv3jx+/fVX\nDh8+0UYiIyPJyTkRjbBSpUp06NCBs846i7POOovOnTvTtm1bKlQoeoQ5T7rFxcWxe/duIiIiuOSS\nS7jjjju46KKLiIw82RFo+vTpjBgxgv3793PttdfywQcfULVqwesIBTF9+nSGDx/OgQMHPJbLZRcL\nVN7rHUYF4WzVvN2JPh0USovxPHbsGLNmfcuUKZOYO3cuALVr1+GGG4bx6qsvFVhv5cpEFi5MYOHC\nBSxcOJ+kJD1+LTo6ms6du9Kv38UMHnwNcXFxheoRrOc/ePAAb7zxKh9+OJ7MzEw6d+7C00+/QNeu\n3byqn5OTw++/L+bLLz9jxoyvPTZwL43ni8BuVX3VOT4CbMLEYojGRNL/uLDkZ75oX1OnTubee0fR\nokVLGjRoSGxsZWJjY52f/D6b35mZmSxbtoTFixeyZMkfpKWdSFRbu3YdunXrTrdu8Tz2WMEprC69\ndCALFvzG/v37j59r0aIlPXueS69e59GjRy8qVarE2rV/smrVSlatWsGqVStJTFxLVtaJ8J4VKlSg\nTZt2nHlmB845J57KlasTHV2JSpUqER1diZiYSlSqFEN0dPTx3xERER4XS6tXr86QIUO58cYRNGnS\n1OPfcNu2rdx66wj++GMxzZs3Z/z4j+jY8Sxv/vxkZGTw9NOP8+GH71OpUiVeeOEV7r13VIHlvWlf\nvjCefYCZqho0/89gG8+//trB5MmTmDx50vHeUs+e5zJ8+EguuuhSypcvX6TV9t27d7NokTGkCxYk\nsG7dGlwuF1FRUfTrdzFDhlzPBRf0LXBYVZTn94UXQFZWFp9+OpFx455n7969NGrUiEcffYrLL7/S\nY6/DExkZGTRsWPCLwkvjOQATxLsfJl/Tr4CoarYTr9Krnqcv2tczzzzJ22+/zowZs4iP71Gkum6y\nsrJYu/ZPFi9eyIoVS/j119889s5z07BhI3r1Oo+ePc+lZ89zOf30uoXWycjIYN26NY5BXcmqVctZ\nu3YNmZmZXutcqVIljhw5UuD1zZt3UqmS96bj2LFjvPzy87z11mtERUXxxBNPc8std3hsZxs2pHDL\nLcNZtWoFIq2LNHdaYuPp5MrJSyRQDxO/L05Vz/BKGz8QLOO5YUMKzzzzJLNmfUt2djZVqlTl2muH\nMHr03dSq1cBncvbs2cO0aV8wZcpk1q79EzDD2quv/if//Of1tGjR8qTygTKeLpeLn36aw1NPPUZS\nklK5chXuvfc+HnnkQVJTszzW9ZVuhe0wEpGXMWlyIzG5m2Y755sQQOM5bNh1fPfdTP78M5natWsX\nqW5+xMVVYdeuQ2zZspnFixdy5523Flj2jz9WFThlUlSOHTuGaiKbNyexadN2jhxJ5+jRoxw5ks6R\nI0eO/xw9euL38uXLCrxfcd30li9fyHXXXc+ePbvp3/9i3nzzPWrUOHVOePr0rxgz5m5SUw8zZMgN\nPP/8uCItSvrCeOZgFodOqY/ZtnmVqn7rtUY+JljG8+KLL2Tp0j9o374Dw4ePZNCgwcTGxvpNvsvl\nYvXqlUydOpmvvvqCgwfNsPbss+MZMuQGBg4cROXKlX1mPIcOHUFq6mHS0lJJTU09/tv9k5aWisvl\nIjIykuuvH8aDDz5C7dq1ffb8vjCevsAX7eu88+LZtm0byclbi90b96RDoP2IAz26yU/+n38mc8cd\nN/Pbbz9Tr1593n//Y+LjjefZkSNHeOyxfzF58kRiYmIZN+51rrrq2uLIKbHxHMapxtMFHALmqar3\nKyJ+IBjGc/XqlVx4YS/69OnHlClfnPQPEQj5R48e5fvvv2Hq1Mn8+uvPuFwuYmJiufzyK7jjjltp\n2bI9ERER5OTk8Pfff7Fly2Y2b97E5s2b2LJl8/GfHTvyS2OTP5UqVSI2tjKVK1c+/rt+/QbcffcY\n2rZtd7ycNZ4nk5OTQ+PGdWjdui0//PCLT3SyxtPIz87O5q23XuOll54jIiKCBx98hEsu+Qe33DKc\ndevW0K5dez78cNIpo7MiyCmwfXnrJD/JCUnXWlXXwfH8JGcCwVvWDSKTJn0EwPDhI33Skygq0dHR\nDBo0mEGDBrN16xY++2wq//vfFKZOnczUqZNp3LgJ5cqVY9u2rfnOUUVGRha6y2f+/D+oXPmEsSxX\nLrD7iUNl59X27dvIyMigefP8klVaSkK5cuUYPfoBunfvyW23jeCFF57hxRefxeVyMWzYTTz99AtE\nR+eXqqnkeOsk3xD4DuNo7G4BZznnFonIQCc/Tlhw6NBBvvrqcxo2bMQFF/QNtjo0bNiI++//F2PG\nPEhCwm98/fX/+Prrr4mNjeWMM9rTqFFjGjVqQqNGjWnc2PyuX78BFSpU8NgraNVKCrxm8Z6UFJMG\nqFmzwlJFFZ/S/KIJhG7x8d2ZO3c+o0ffxaJFCbz88utcdtkVfpXprRfs65j5zePaqOosJy3HZ8Ar\nwDCfa1dK+eKL/5Gens7o0cMD3hvzRGRkJL16nccVVwxg165DQekRW07FbTybN/ef8bRAjRo1+eST\nqeTk5JziJ+oPvDWe5wNDVXVl7pOquk5EnsCk5AgLXC4XEydOoHz58gwZUuiu1aDhreEszT2WUGHD\nBms8A0kgDCd4H88TzJC9oHsUbQtMGWbhwgSSkpR//OMyrxzWLZYTw3Y75xlKeGs8fwLGikij3Ced\nudCngB99rFepZdKkCQAMGzYyyJpYygopKcnExdWmalUb8jaU8HbYfj+QAKwXkTWY1LNxwBmYrW5j\n/KNe6WLnzp18++1M2rRpS7duNpKZpXAyMzPZunULZ59dqC++pYzhravSFhFpC4wAugM1gA3AfzB7\ng/P18xSRisBEoBnGJ3QUJqDIN8B6p9h4Vf0sV51I4D1M5PoMYKSqJhf90XzP1KmfcuzYMW688Sa7\nGGPxis2bN5GTk2PnO0OQosScSgXmuPOze+nneTOQqqrxIiLAO8AXwGvuYA35cDkQrardRSQeeBW4\nrAh6+oXs7GwmT55ETEwsV111TbDVsZQR3POdTZva+c5Qw6s5T2duczUnp+I4C5gDzBeRgkKBtwW+\nB1BVBdoAnYFLReRXEflIRKrkqdMTmOXUWQQELVpTbn78cQ7btm1l8OBrqFKl6KGwLOGJdVMKXYri\n5wlF9/NcAQwQkelAN6A+sASYoKpLReRR4EmcCN8OVYHc0wDZIhKlqgVGmqhePeaUbHpxcXltcsmY\nOnUSAGPG3O3VvX0tv6iEu/zSgnVTCl387ef5Maa3+RtmwWkp8JWqugM1TgPezlPnEJD7Py/Sk+EE\nTsnf7Ou95Zs2bWTWrFl07dqNevWaFXrvYAcjDif5pd1Ip6QkExERUWisSkvZw99+nl2Bn1S1J2au\ncwMwW0TOdq5fiDGouUkALgFw5jxXF0FHv/DppxOP75W1WIpCSkoyDRs28tv+akvw8Lbn6fbzXKyq\nW9wnnbnQsRTs57keeMYZnh/AJN06HXhbRI4BfwO3OPf6FHgM0xvtKyILMFtCS55spARkZGTw3/9O\npkaNGvzjH5cHUxVLGSM19TA7d/5N794XBFsVix/wq5+nEyykT57TOzDZDPOWzb3X8TYv9fI7M2dO\nZ+/evdx5572292ApEhs2pAB2vjNU8WrY7vQ22wIPAklOvQ3AQ5jV8aD2Dv3JxIkTiIiIYOjQkH1E\ni5+wxjO08drPU1UPY1JuvCki5TH+mDcB4zDG9Cl/KBhM1qz5kz/+WMwFF/SxE/6WImP3tIc2RUrM\nLCKtgZHADUAtzJD9HWCK71ULPicCHt8cZE0sZZFAxPG0BI9CjaeIVAKuwRjN7ph815WAO4EPVDXH\nQ/Uyy+HDh/jyy89o0KAhffr0C7Y6ljLIhg3JlC9fnoYNGxVe2FLmKHDOU0S6iMj7mBXxCUAaMBRo\nhVkFXxOqhhPgiy8+Iy0tlRtuGFaqAh5bygYul4uUlBSaNm1m20+I4qnn+TuwBngC+FxV/wIQkZCP\nq+Vyufjkk4+Iiooq1QGPLaWXvXv3cvDgAbp3P8WxxBIieDKeKzGBP4YCcSIyxZ38LdRZvHgR69at\n5fLLr6BOnTrBVsdSAkTkYWAgUAETrWshZkdcBMYPeWRhO9iKg93THvoUOGxX1U4Y4/kTZt/6nyKy\nBBNWzkX+edxDAhvwODQQkd5AD4xf8XlAQ+B54BFVdXcJ/+EP2XZPe+jj0c9TVdeo6oNAI8yWSQUe\nwby1x4nIHSISUl2z3bt3M3PmdERa2yFX2ac/ZnvvNGAmJirYlar6q4hUwOx2yzcWbUmxPp6hj7fB\nkHOA2Zh96ZWBqzDuSm9j/D7nq+r5/lMzcPz3v5OdgMcjbMDjsk8toDEwAGgKzABai0hjzJbig5jp\nKY8UJ2rXtm2bADj77I5+C14S7KAo4S6/SH6eAKqaiokOP9HZ2z4UuN7XigWD7OxsPv10IjExMVx9\n9T+DrY6l5OwFElU1E1AROQrEqepmoKWIjAReA270dJPiRO1auzaR2NjKREbG+CXCVDhFzgqmfE8G\nukQ5OlV1q6o+p6ptSnKf0sLcuT+wZctmrrzyapusKzSYD1wkIhEiUg+IBT4SkZbO9cOAz93tcnJy\n2LgxhebNW9jRSwhT5J5nKOPeUWRDz4UGqvqNiJyLcbuLxCx2HgYmiUgmkI7Z/OFTduzYztGjR2ne\n3G7LDGWs8XTYsmUzP/44h86du9K+fYdgq2PxEc6CZ178uhJot2WGByUatocSX3/9BS6XixtvHBFs\nVSxlHOvjGR5Y4+kwf/5vAPTte1GQNbGUdayPZ3hgjSeQmZnJH38sok2bttSsWTPY6ljKOG4fTxuK\nLrSxxhNYsWI5R44csU7xFp+QkpJMrVpxnHZatWCrYvEj1ngCCxfOB+Ccc3oFWRNLWSczM5MtWzbb\nXmcYYI0nsGCBMZ7x8bbnaSkZW7ZsJjs72853hgFhbzyzsrL4/ffFtGolxMXFBVsdSxnHrrSHD371\n8xSRipitnM2AQ8AoVV3vXBsC3KWq3fOpt8wpD7BRVf2WfW3VqhWkpaXSvXtPf4mwhBHWxzN88LeT\n/M1AqqrGi4hg8h31F5FOmORxp+xdE5FoIEJVe/tZNwAWLEgAoEcPO2S3lBzb8wwf/D1sbwt8D6Cq\nCrQRkZqYmIr3FlCnAxAjInNEZK6IxPtTwQULjH9njx6252kpORs2JBMREWGzrYYB/u55rgAGiMh0\noBsmGO0kYAwmkVx+pAOvYPImtQS+FxHxFO27OCHDwERR+v33RbRs2ZIzzmhZaPmiEOxwWeEuP1ik\npCTToEFDKlWqFGxVLH7G38bzY6AN8BuQgIk+3wwYD0QDbUXkDVXN3QtNApJV1QUkicheoC6wtSAh\nxQkZBrBy5XIOHTrEwIGDfBreKlzCdZUG+aXJSKempvL3339x3nkhEdrWUgj+HrZ3BX5S1Z7AF5hE\ncu2c+cxrgbV5DCfACOBVACeMWFXgL38od2K+0w7ZLSVn48YNgJ3vDBf83fNcDzwjIo8CBzCLRPki\nIp8CjwEfYUKGzcf0VEf4I0EXnHCOt8bT4gvce9qtg3x44Ffjqap7gD4FXNsExOc6zp3jd4g/9QIT\nsHbRogU0btyEevXq+1ucJQywK+3hRdg6ya9du4YDBw7YXqfFZ1gfz/AibI2n20XJBgOx+IoNG5Ip\nX748DRs2CrYqlgAQxsbTLhZZfIfL5SI5OZkmTZoSFWUTNIQDYWk8zXxnAg0aNKRRo8bBVscSAuzb\nt4+DBw/Y+c4wIiyNp2oi+/bts0N2i884sdJujWe4EJbG0x2Czg7ZLb7CrrSHH2FpPBcutPOdFt9i\nU2+EH2E3s+1yuViwYD5169azwRvCABF5GBgIVADeA5YCbwPZQAYwVFV3llSO7XmGH2HX80xOXs+e\nPbvp3v0cIiJOiYhnCSFEpDfQA5On/TxMYJo3MXFkewNfAw/5QlZKSjIxMbHUqXO6L25nKQOEXc8z\nIcGGoAsj+gOrgWmYGAkPAB+oqjtWQhRwtKRCcnJy2LgxhRYtWtkXchgRdsbT7mcPK2oBjYEBQFNg\nBtAaQER6AHcC5xZ2k8JCHm7dupUjR47Qtm3rgEZ5CnZEqXCXH1bG08x3JhAXV9vOTYUHe4FEVc0E\nVESOAnEicj7wKHCpqu4u7CaFhTz8/fcVANSv3zigofjCJexgMOV7MtBhNee5cWMKO3f+TY8ePe3w\nKjyYD1wkIhFOeMNY4GJMj7O3qm7whRC7WBSehJXxdG/JtM7x4YGqfgMsB34HZgKjgNeBKsDXIvKz\niIwtqRy3m5I1nuFFWA3b3c7x55zTK8iaWAKFqj6Y51QNX8uwcTzDk7DpebpcLhYuTKBWrVq0aiXB\nVscSQqSkJFOzZk2qVasebFUsASRsjOfmzZvYvn0b8fHWv9PiO44dO8bmzZvsnvYwJGyM54ktmXa+\n0+I7tmzZRHZ2tp3vDEPCxni65zu7d7f+nRbfYVfaw5ewMZ4LFyZQvXp12rRpG2xVLCFESoo7IIg1\nnuFGWBjPbdu2smXLZrp160FkZFg8siVA2J5n+OJXVyURqQhMBJoBh4BRqrreuTYEE6Che546kZjo\nNx0wUW9GqmpySfQ4Eb/TzndafMvGjabn2bRpsyBrYgk0/u6G3Qykqmo8cBfwDoCIdMLkcM9v2fty\nINoxqv8CXi2pEjZ+p8VfpKQk06BBQypVqhRsVSwBxt/Gsy3wPYCqKtBGRGoCzwP3FlCnJzDLqbMI\n6FJSJRISfqNq1dNo1659SW9lsRwnLS2NHTu207SpdY4PR/y9w2gFMEBEpgPdMPEUJwFjgCMF1KkK\nHMx1nC0iUaqaVZAQT1Fvtm/fzqZNGxkwYACnn16tuM9RZIId8SXc5QeCjRvN1vjmza3xDEf8bTw/\nBtoAvwEJgAsz/zkeiAbaisgbqpq7F3oIs/fYTaQnwwmeo97MnDkLgM6d423EmxCUH0wj7d6WaReL\nwhN/D9u7Aj+pak/gC+BzVW3nRPG+Flibx3CCMbKXAIhIPCaYbbE5kZ/dLhZZfItdaQ9v/N3zXA88\nIyKPAgcwi0T5IiKfAo9hon73FZEFmAWl4SVRYOHC+VSuXIX27TuU5DYWyym4jaf18QxP/Go8VXUP\n0KeAa5uA+FzHQ3Ndvs0X8nfu3Ely8nouuKAPUVFhFUDKEgBSUpKJioqiUaPGwVbFEgRC2mN80SLr\nomTxHxs2JNOkSVP7Yg5TQtp4upO92eDHFl+zb99e9u/fb+c7w5iQNp4LFyYQExNDx45nBVsVS4jh\njh5v5zvDl5A1nnv27EE1ka5du1G+fPlgq2MJMU4sFlkfz3AlZI2n3ZJp8SfWx9MS4XK5gq1Didm9\n+/Dxh6hdu2qB5XbtOhQQfcLJST3Y8uPiqvg9LUBpa18QXt9xMOV7al8h2/O0WCwWf2KNp8VisRQD\n66BmCWlE5GFgIFABeE9VP3LOv44J9vV+MPWzlF1sz9MSsohIb6AHcA5wHtBQROJE5HuMQbVYio3t\neVpCmf6YwDLTMKEOHwAqA08BFwdPLUsoYI2nJZSpBTQGBgBNgRlAa1XdKCJeG8/84sXmR6DD4wU7\nZmq4yw8547lr16Ggu1FYSg17gURVzQRURI4CccCuotwkd7xYT+0rkG0u2G08XOR7MtAhYTzz88UK\n9lvJyi8VkeTnA/eIyGtAXSAWY1CLRGlsX6VBh3CXbxeMLCGLqn4DLAd+B2ZisrdmB1crS6gQEjuM\nLBaLJdDYnqfFYrEUA2s8LRaLpRhY42mxWCzFwBpPi8ViKQbWeFosFksxCAk/TzciEgm8B3QAMoCR\nqprsZ5nlgY+BJkBF4FlgK/ANJvUywHhV/cyPOiwD3MEkNwLPAZMAF/AnxkUnx0+yhwHDnMNooCPQ\nnQA+f6Cw7Quw7es4IWU8gcuBaFXtLiLxwKvAZX6WeT2wV1VvEJEawArgaeA1VX3Vz7IRkWggQlV7\n5zo3A3hMVX8Wkfcxf4Np/pCvqpMw/0iIyLuYf/TOBOj5A4xtX9j25SbUjGdPYBaAqi4SkS4BkPkF\n8KXzOQLIwny5IiKXYd6O96qqv/aSdQBiRGQO5vt8xJH/i3P9e6Affmrcbpy/dTtVHSUi4wnc8wcS\n275s+zpOqM15VgUO5jrOFhG/viBUNVVVD4tIFUwjfwyzo+UBVT0X2AA86UcV0oFXMBGEbgOmYHoK\n7t0Ph4HT/CjfzSPAWOdzIJ8/kNj2ZdvXcULNeB4Ccm94jVTVLH8LFZGGwDxgsqpOBaap6lLn8jSg\nkx/FJwH/UVWXqiZh9m7XyXW9CnDAj/IRkWqAqOo851Qgnz+Q2PZl29dxQs14JgCXADhzUqv9LVBE\n6gBzgIdU9WPn9GwROdv5fCGwNN/KvmEEZu4NEamH6R3NcQIBg4lb+Zsf5QOcC/yU6ziQzx9IbPuy\n7ft9AtIAAAcSSURBVOs4oTbnOQ3oKyILMPNDwwMg8xGgOvC4iDzunBsDvC4ix4C/gVv8KP8jYJKI\nzMesfo4A9gAfikgFYB0n5sz8hWCGT25uB94O0PMHEtu+bPs6jg0MYrFYLMUg1HqeFj/h+DgeAPqq\n6mIRuQp4WlXb5FN2ACYEXC9VnZ/rfB3gLyATqK6qR3Jduxr4DGgHXA3cr6qVS6jzJuAbVb2zJPcp\nosyKwGiMi1FzjD/oCuBtVf0qV7mfgVRVHRAo3Sy+JdTmPC3+4wygPLDMOe4OLCig7K9ANhCf53wf\nzGp1Bcw8Vm56An+p6lpgAnC+D3QOBp8A92GGu5cCN2CGnF+KyO25yt3hlLOUUWzP0+It3YGlqnos\n1/FH+RVU1UPOrpTueS71AeZiemR9gdm5rp3jXENVtwHbfKd6YBCRJsA1wLV5drzMdFyNxgLjAZyX\nhKUMY42nxSPO0LdxruPck+TxItJTVYflU/VnzNA1N32Al4HNGOPpvmdljDP2O87xU+QatjsyhwMX\nYXpzGcB/nDJZTpnTgbcxDtupwMP5PEst4AXMCnENYBHwoKouEZGOmKjz56vqz075e4HXgQtVda5z\n7j7gQeD0XL6ObuKc3/mN6F4GFotIeVU9lnvYLiKTgBvzqQPQVFU3iUgs8CJmSqMqsBgYrarLC6hn\n8TN22G4pjEGYHuRGjIN2d8yK61FMTvRnCqg3D6grIo0BRKQ10AD4AfgR+P/2zjVEyjKK4z+zIkQQ\nESSIPkkcMu1Lil101axdaKsvZSlBJLEgVFQUEmRklBDdKCkw/FBUhlKIEeiSK5umruYQbhb4pzZE\n7aJSFmlXsT6cM+07szPv2MD66fzgZd59LjPPPMP+3/Oc81ymhw8UfHg/ltrpKPW8DBzHl0i+BjwI\n9MR7j8Wt2Bl45PUR3Mq7pFo5BHoXLuCP4RbiGGC7mU0HBvHI7fWFz5wXr7MLaV1AbwPhBPgc+BZY\nbWbPm9ncWN6IpL2SXihY7kWexvu1enUBJ3BL/LCZjcFP/lyE/wYL8f7/2MymNO+yZDRJ8UxKCctm\nELgUeF/Sbtz3uU/SgKShJlU/wZcSVofuNwJHJB3Al/adxoUMXJy+lnSopCm7JD0gaaukJ3Er8abI\n6wauBBZLWi9pHXAbtSOrJbi7oFvSO5I+xEXqOLAixLCX8LVGgKwjPmdOpF0U95ua9NWf0ZbDwKO4\n9f2zmW0xszubfTFJQ5J2R9/uAe7DV/YsijOXOnFRXyxpTZzNdDMu9o+X9FkyiqR4JqWEVTcDOAV8\nE8sRrwYqZnZ+iMwIJJ3EJy9XxfMG3OJE0il8yLwg8mZTbnUS5YscwU/DBPeXngjxqX7+Z8DBQvkO\n4MuirzGOJN4AzI2kXmCWmY3DxXg88Arunhgb73EBtb7a+u89CEyPNq0kXAHAOjN7t8V3BFiBuxUW\nSjoeafNxMd0WfV59KHzEcB8m55j0eSatGGLY5/lXXd79eHT5niZ1+4EF8c8+D48wV+kD7g5RmgW8\n3qIdv9X9fYbhh/9EfOJ2PT8U7icCRxuUOYr7EMHF6DxczKcClUgbjy8B7AQGJJUuRwwrdldcy81s\nMu6PXWxmawrLDGuIjS6ewDe6GChkTQLGMbL/ARq5AZJzQFqeSStuwYepbwEzcYvqHzxwMRO3lJrR\njweCrsEFqq+Q14cPoztxC7KhoJwlPwKTG6RPKtz/RO2a7CoXR30kncA3nZiPW5nbJX2P79wzB3c9\nbG7WCDN70cwq9emSjjG8CmbEvNioeznwNrBe0qq67F+AY3h/11/XNmtPMrqkeCalSNqPC1O/pAoe\nyf4b2CipIulgSfWdeFBmKbBfUtHy+xTfaKMHGJTUyHI8W/qBCWb2X7DHzAwX5yo7gCtCpKplLsQD\nYjsL5Tbj4nkdPl+VeL0DH4439HcGXwFXmVlng7zL4vWL+gwzmwBsxF0RPQ3q7sAj+SejzyvxW9zF\nyBkNyTkih+1JKeHTnMrwJhjTgANNosY1SDplZnvx6PCrdXmnzWwbHviot7T+L1twgVtrZstw/+wz\n1A5z3wAeAjaZ2XLcmnsYt0ZXFsr14psNn8FFi3jve4HvJO0racebuAtjY2zcuxX4Hd//chnwgaTt\nDeqtxXeKXwRMrfMjD+GrtfZG258CDgG3426QpSXtSUaRtDyTVkzBjz+oBlqm4VNyzpZ+PMjS1yCv\nL/JaBYtKCR/jrbjwrQJW40I2WCjzKz4U34NPdVqHC2RH3VzJCh6BH5RUPXqiuPFvWTv+wKPiz+JD\n/PeiTUuAl/CHSCO68VVXG6J9A4WrOyLuXfhD4jnc+p0DLJHUylecjBK5MUiSJEkbpOWZJEnSBime\nSZIkbZDimSRJ0gYpnkmSJG2Q4pkkSdIGKZ5JkiRtkOKZJEnSBimeSZIkbZDimSRJ0gb/AvFKfXDY\n/kwwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d565d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig=plt.figure(figsize=(5,4.0))\n",
    "\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, sharex=True, figsize=(5,4.0))\n",
    "\n",
    "\n",
    "stride = max( int(len(x) / 3), 1)\n",
    "\n",
    "\n",
    "ax1.plot(x,y_1,color='k', marker= 's', markevery=stride), ax1.set_title('Yahoo!')\n",
    "ax2.plot(x,y_2,color='k',  marker= 's', markevery=stride), ax2.set_title('DBPedia')\n",
    "ax3.plot(x,y_3,color='k',  marker= 's', markevery=stride), ax3.set_title('Yelp Polarity')\n",
    "ax4.plot(x,y_4,color='k',  marker= 's', markevery=stride), ax4.set_title('Yelp Full')\n",
    "\n",
    "plt.xlabel('# Window Size', fontsize=16)\n",
    "plt.ylabel('Accuracy (%)', fontsize=16)\n",
    "ax = plt.gca() \n",
    "ax.yaxis.set_label_coords(-1.41, 1.1) \n",
    "ax.xaxis.set_label_coords(-0.10, -0.16) \n",
    "\n",
    "fig.savefig('filter_size.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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